During transmission, the wireless signal not only arrives at a receiving terminal through a direct path between a transmitting and a receiving device, but meanwhile arrives at the receiving terminal through the reflection of objects (ground, people, wall, furniture, etc.) in a transmission environment as well. This is also known as multi-path propagation. If one of the transmitting terminal, the reflector, or the receiving terminal in a signal path is in constant motion, this would lead to an offset between the frequency of the signal measured at the receiving terminal and the carrier frequency of the signal transmitted at the transmitting terminal. Such frequency shift is called a Doppler frequency shift. When there is a moving object in the transmission environment, a wireless signal reflected by this object will also be received at the receiving terminal. If the Doppler frequency shift of the signal reflected by the moving object can be recognized, a speed characteristics of the moving object can be obtained therefrom, so as to analyze the state of the moving object (for example, the speed variation, walk, run, falling down, etc.) or track the moving object, and so on.
However, nowadays, the carrier signal frequency commonly used in the commercial radio frequency communication systems (Wi-Fi, RFID, cellular networks, etc.) are between hundreds of megabytes Hertz and dozens of gigabytes Hertz. In such a high carrier frequency wireless system, the Doppler frequency shift introduced by the moving objects is merely a few dozens of Hertz. A highly accurate sampling of the frequency domain is required in case of intending to extract such “tiny” Doppler frequency shift from such high carrier frequency signal.
In 1986, an American Ralph O. Schmidt proposed a Multiple Signal Classification (MUSIC) algorithm in a literature 1 (Multiple emitter location and signal parameter estimation, IEEE transactions on antennas and propagation, AP-34(3):276-280, March 1986, the entire contents of which is hereby incorporated by reference in its entirety for all purposes), which could distinguish multiple incident signals superposed at an antenna array of the receiving terminal by means of decomposing eigenvalues of an autocorrelation matrix, and estimates the respective arrival angles thereof. The MUSIC algorithm is a spatial spectral estimation algorithm that separates the signal subspace and the noise subspace with a covariance matrix of the received data, generates a space scan spectrum with the orthogonality among the signal steering vectors and the noise subspace, searches a spectral peak over the entire domain, so as to obtain the angle estimation of the signal. The MUSIC algorithm could achieve a high resolution of the direction-finding; asymptotical unbiased estimation on the number of signal, DOA (direction of arrival), polarization, the intensity of noise interference, the intensity of an incoming wave, and coherence relationship; the capability of resolving DOA estimation problem for a multi-path signal; the capability of wireless direction-finding in a dense signal environment. Besides arrival angular information, the MUSIC algorithm also can be applied to wireless signal frequency estimation.
In 2013, an American Qifan PU proposed a solution in a literature 2 (Whole-home gesture recognition using wireless signals, MobiCom13, the entire contents of which is hereby incorporated by reference in its entirety for all purposes): during the digital signal processing phase of a Wi-Fi signal, it obtains sufficiently long samplings of a time-domain signal by splicing multiple Wi-Fi signal symbols, afterwards obtaining a high-precision frequency-domain sampling by a Fast Fourier Transform. However, this method requires that a conventional Wi-Fi signal receiver is modified, such that multiple Wi-Fi signal symbols at the receiving terminal can be spliced. For a common commercial wireless transceiver, such as a Wi-Fi network card, a router, a RFID reader, etc., since its digital signal processing module has been determined, therefore this method cannot be applied.
In the field of wireless communication, Channel State Information (CSI) is a channel attribute of a communication link, which describes attenuation factors of a signal on each transmission path, that is, the value of each element in a channel gain matrix could reveal signal scatter, multi-path delay, Doppler frequency shift, a rank of a MIMO channel, a beam-forming vector, environmental attenuation, distance attenuation, and the like. The CSI enables a communication system to adapt to the current channel conditions and provides a guarantee for high reliability and high rate communication in a multi-antenna system.
In 2015, a Chinese Wei Wang in P.R.C. established a CSI-Speed model in a literature 3 (Understanding and Modeling of Wi-Fi Signal Based Human Activity Recognition, MobiCom15, the entire contents of which is hereby incorporated by reference in its entirety for all purposes), proposing that: the speed characteristics of a signal reflected by a moving object can be obtained from an amplitude of the Chanel State Information (CSI) of a Wi-Fi signal, and then it is applied to behavior identification.
In 2016, a Chinese Kun Qian gave a practical explanation on the CSI-Speed model in a literature 4 (Decimeter Level Passive Tracking with Wi-Fi, Hot Wireless 16, the entire contents of which is hereby incorporated by reference in its entirety for all purposes): a variation frequency of CSI amplitude reflects the Doppler frequency shift of the signal reflected by the moving object. However, the Doppler frequency shift information estimated in this way includes only a magnitude of the frequency shift, and does not include a direction of the frequency shift (forward/reverse shift), thus the complete Doppler frequency shift information cannot be obtained.